Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006
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Transcript of Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006
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Lecture 14: Scientific Visualization
Information VisualizationCPSC 533C, Fall 2006
Tamara Munzner
UBC Computer Science
26 Oct 2006
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Credits
• almost unchanged from lecture by Melanie Tory (University of Victoria)– who in turn used resources from– Torsten Möller (Simon Fraser University)– Raghu Machiraju (Ohio State University)– Klaus Mueller (SUNY Stony Brook)
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News
• Reminder: no class next week – I'm at InfoVis/Vis in Baltimore
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Difference between SciVis and InfoVis
Direct Volume Rendering
Streamlines
Line Integral Convolution
GlyphsIsosurfaces
SciVis
Scatter Plots
Parallel Coordinates
Node-link Diagrams
InfoVis[Verma et al.,Vis 2000]
[Hauser et al.,Vis 2000]
[Cabral & Leedom,SIGGRAPH 1993]
[Fua et al., Vis 1999]
[http://www.axon.com/gn_Acuity.html]
[Lamping et al., CHI 1995]
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Difference between SciVis and InfoVis
• Card, Mackinlay, & Shneiderman:– SciVis: Scientific, physically based– InfoVis: Abstract
• Munzner:– SciVis: Spatial layout given– InfoVis: Spatial layout chosen
• Tory & Möller:– SciVis: Spatial layout given + Continuous– InfoVis: Spatial layout chosen + Discrete– Everything else -- ?
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Medical Scanning
• MRI, CT, SPECT, PET, ultrasound
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Medical Scanning - Applications
• Medical education for anatomy, surgery, etc.• Illustration of medical procedures to the patient
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Medical Scanning - Applications
• Surgical simulation for treatment planning• Tele-medicine• Inter-operative visualization in brain surgery,
biopsies, etc.
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Biological Scanning
• Scanners: Biological scanners, electronic microscopes, confocal microscopes
• Apps – physiology, paleontology, microscopic analysis…
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Industrial Scanning
• Planning (e.g., log scanning)• Quality control• Security (e.g. airport scanners)
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Scientific Computation - Domain
• Mathematical analysis
• ODE/PDE (ordinary and partialdifferential equations)
• Finite element analysis (FE)
• Supercomputer simulations
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Scientific Computation - Apps
• Flow Visualization
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Isosurfaces - Examples
Isolines Isosurfaces
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Isosurface Extraction
• by contouring– closed contours– continuous– determined by iso-value
• several methods– marching cubes is most
common
1 2 3 4 3
2 7 8 6 2
3 7 9 7 3
1 3 6 6 3
0 1 1 3 2
Iso-value = 5
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MC 1: Create a Cube
• Consider a Cube defined by eight data values:
(i,j,k) (i+1,j,k)
(i,j+1,k)
(i,j,k+1)
(i,j+1,k+1)
(i+1,j+1,k+1)
(i+1,j+1,k)
(i+1,j,k+1)
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MC 2: Classify Each Voxel• Classify each voxel according to whether it lies
outside the surface (value > iso-surface value)inside the surface (value <= iso-surface value)
8Iso=78
8
55
10
10
10
Iso=9
=inside=outside
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MC 3: Build An Index
• Use the binary labeling of each voxel to create an index
v1 v2
v6
v3v4
v7v8
v5
inside =1outside=0
11110100
00110000
Index:v1 v2 v3 v4 v5 v6 v7 v8
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MC 4: Lookup Edge List
• For a given index, access an array storing a list of edges
• all 256 cases can be derived from 15 base cases
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MC 4: Example
• Index = 00000001• triangle 1 = a, b, c
a
b
c
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MC 5: Interp. Triangle Vertex
• For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values
=10=0
T=8
T=5
i i+1x
iviv
ivTix
1
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MC 6: Compute Normals• Calculate the normal at each cube vertex
1,,1,,
,1,,1,
,,1,,1
kjikjiz
kjikjiy
kjikjix
vvG
vvG
vvG
• Use linear interpolation to compute the polygon vertex normal
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MC 7: Render!
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Direct Volume Rendering Examples
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Rendering Pipeline (RP)
Classify
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Classification
• original data set has application specific values (temperature, velocity, proton density, etc.)
• assign these to color/opacity values to make sense of data
• achieved through transfer functions
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Transfer Functions (TF’s)
• Simple (usual) case: Map data value f to color and opacity
Human Tooth CT
(f)RGB(f)
f
RGB
Shading,Compositing…
Gordon Kindlmann
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TF’s
• Setting transfer functions is difficult, unintuitive, and slow
f
f
f
f
Gordon Kindlmann
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Transfer Function Challenges
• Better interfaces:
– Make space of TFs less confusing
– Remove excess “flexibility”
– Provide guidance
• Automatic / semi-automatic transfer function generation
– Typically highlight boundaries
Gordon Kindlmann
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Rendering Pipeline (RP)
Classify
Shade
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Light Effects
• Usually only considering reflected part
Light
absorbed
transmitted
reflected
Light=refl.+absorbed+trans.
Light
ambient
specular
diffuse
ssddaa IkIkIkI
Light=ambient+diffuse+specular
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Rendering Pipeline (RP)
Classify
Shade
Interpolate
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Interpolation
• Given:
• Needed:
2D 1D• Given:
• Needed:
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Interpolation
• Very important; regardless of algorithm• Expensive => done very often for one image• Requirements for good reconstruction
– performance– stability of the numerical algorithm– accuracy
Nearestneighbor
Linear
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Rendering Pipeline (RP)
Classify
Shade
Interpolate
Composite
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Ray Traversal Schemes
Depth
IntensityMax
Average
Accumulate
First
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Ray Traversal - First
Depth
Intensity
First
• First: extracts iso-surfaces (again!)done by Tuy&Tuy ’84
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Ray Traversal - Average
Depth
Intensity
Average
• Average: produces basically an X-ray picture
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Ray Traversal - MIP
Depth
IntensityMax
• Max: Maximum Intensity Projectionused for Magnetic Resonance Angiogram
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Ray Traversal - Accumulate
Depth
Intensity
Accumulate
• Accumulate: make transparent layers visible!Levoy ‘88
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Volumetric Ray Integration
color
opacity
object (color, opacity)
1.0
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Flow Visualization
• Traditionally – Experimental Flow Vis• Now – Computational Simulation
• Typical Applications:– Study physics of fluid flow– Design aerodynamic objects
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Traditional Flow Experiments
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Techniques
Contours
StreamlinesJean M. Favre
Glyphs (arrows)
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Techniques
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Techniques - Stream-ribbon
• Trace one streamline and a constant size vector with it
• Allows you to see places where flow twists
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Techniques - Stream-tube
• Generate a stream-line and widen it to a tube• Width can encode another variable
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Mappings - Flow Volumes
• Instead of tracing a line - trace a small polyhedron
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LIC (Line Integral Convolution)
• Integrate noise texture along a streamline
H.W. Shen
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Overview
• What is SciVis?• Data & Applications• Iso-surfaces• Direct Volume Rendering• Vector Visualization• Challenges
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Challenges - Accuracy
• Need metrics -> perceptual metric
(a) Original (b) Bias-Added (c) Edge-Distorted
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Challenges - Accuracy
• Deal with unreliable data (noise, ultrasound)
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Challenges - Accuracy
• Irregular data sets
regular rectilinearuniform curvilinear
Structured Grids:
regular irregular hybrid curved
Unstructured Grids:
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Challenges - Speed/Size
• Efficient algorithms• Hardware developments (VolumePro)• Utilize current hardware (nVidia, ATI)• Compression schemes• Terabyte data sets
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Challenges - HCI
• Need better interfaces
• Which method
is best?
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Challenges - HCI• “Augmented” reality• Explore novel I/O devices